Lucas R.F. Henneman

Bayesian Belief Networks for predicting drinking water distribution system pipe breaks

Abstract In this paper, we use Bayesian Belief Networks (BBNs) to construct a knowledge model for pipe breaks in a water zone. To the authors’ knowledge, this is the first attempt to model drinking water distribution system pipe breaks using BBNs. Development of expert systems such as BBNs for analyzing drinking water distribution system data is not only important for pipe break prediction, but is also a first step in preventing water loss and water quality deterioration through the application of machine learning techniques to facilitate data-based distribution system monitoring and asset management. Due to the difficulties in collecting, preparing, and managing drinking water distribution system data, most pipe break models can be classified as “statistical–physical” or “hypothesis-generating.” We develop the BBN with the hope of contributing to the “hypothesis-generating” class of models, while demonstrating the possibility that BBNs might also be used as “statistical–physical” models. Our model is learned from pipe breaks and covariate data from a mid-Atlantic United States (U.S.) drinking water distribution system network. BBN models are learned using a constraint-based method, a score-based method, and a hybrid method. Model evaluation is based on log-likelihood scoring. Sensitivity analysis using mutual information criterion is also reported. While our results indicate general agreement with prior results reported in pipe break modeling studies, they also suggest that it may be difficult to select among model alternatives. This model uncertainty may mean that more research is needed for understanding whether additional pipe break risk factors beyond age, break history, pipe material, and pipe diameter might be important for asset management planning.

Evaluating the Effectiveness of Air Quality Regulations: A Review of Accountability Studies and Frameworks.

ABSTRACT Assessments of past environmental policies—termed accountability studies—contribute important information to the decision-making process used to review the efficacy of past policies, and subsequently aid in the development of effective new policies. These studies have used a variety of methods that have achieved varying levels of success at linking improvements in air quality and/or health to regulations. The Health Effects Institute defines the air pollution accountability framework as a chain of events that includes the regulation of interest, air quality, exposure/dose, and health outcomes, and suggests that accountability research should address impacts for each of these linkages. Early accountability studies investigated short-term, local regulatory actions (for example, coal use banned city-wide on a specific date or traffic pattern changes made for Olympic Games). Recent studies assessed regulations implemented over longer time and larger spatial scales. Studies on broader scales require accountability research methods that account for effects of confounding factors that increase over time and space. Improved estimates of appropriate baseline levels (sometimes termed “counterfactual”—the expected state in a scenario without an intervention) that account for confounders and uncertainties at each link in the accountability chain will help estimate causality with greater certainty. In the direct accountability framework, researchers link outcomes with regulations using statistical methods that bypass the link-by-link approach of classical accountability. Direct accountability results and methods complement the classical approach. New studies should take advantage of advanced planning for accountability studies, new data sources (such as satellite measurements), and new statistical methods. Evaluation of new methods and data sources is necessary to improve investigations of long-term regulations, and associated uncertainty should be accounted for at each link to provide a confidence estimate of air quality regulation effectiveness. The final step in any accountability is the comparison of results with the proposed benefits of an air quality policy. Implications: The field of air pollution accountability continues to grow in importance to a number of stakeholders. Two frameworks, the classical accountability chain and direct accountability, have been used to estimate impacts of regulatory actions, and both require careful attention to confounders and uncertainties. Researchers should continue to develop and evaluate both methods as they investigate current and future air pollution regulations.

Accountability assessment of regulatory impacts on ozone and PM2.5 concentrations using statistical and deterministic pollutant sensitivities

Since the 1990 Clean Air Act Amendments, the USA has seen dramatic decreases in air pollutant emissions from a wide variety of source sectors, which have led to changes in pollutant concentrations: both up and down. Multiple stakeholders, including policy-makers, industry, and public health professionals, seek to quantify the benefits of regulations on air pollution and public health, a major focus of air pollution accountability research. Two methods, one empirical, the other based on a chemical transport model (CTM), are used to calculate the sensitivities of ozone (O3) and particulate matter with diameters less than 2.5 μm (PM2.5) to electricity-generating unit (EGU) and mobile source emissions. Both methods are applied to determine impacts of controls on daily concentrations (which are important in assessing acute health responses to air pollution), accounting for nonlinear, meteorologically, and emission-dependent responses of pollutant concentrations. The statistical method separates contributions of nearby EGU, regional EGU, and mobile source emissions on ambient city-center concentrations. Counterfactual emissions, an estimate of emissions under a scenario where no new controls were implemented on local EGU sources after 1995, regional EGUs after 1997, and mobile sources after 1993, are combined with these sensitivities to estimate counterfactual concentrations that represent what daily air quality in Atlanta, GA would have been had controls not been implemented and other emissions-reducing actions not been taken. Regulatory programs are linked with reduced peak summertime O3, but have had little effect on annual median concentrations at the city-center monitoring site, and led to increases in pollutant levels under less photochemically-active conditions. The empirical method and the CTM method found similar relationships between ozone concentrations and ozone sensitivity to anthropogenic emissions. Compared to the counterfactual between 2010 and 2013, the number of days on which O3 (PM2.5) concentrations exceeded 60 ppb (12.0 μgm−3) was reduced from 396 to 200 (1391 to 222). In 2013, average daily ambient O3 and PM2.5 concentrations were reduced by 1.0 ppb (2 %) and 9.9 μgm−3 (48 %), respectively, and fourth highest maximum daily average 8-h O3 was reduced by 14 ppb. Comparison of model-derived sensitivities to those derived using empirical methods show coherence, but some important differences, such as the O3 concentration where the sensitivity to NOx emissions changes sign.

Responses in Ozone and Its Production Efficiency Attributable to Recent and Future Emissions Changes in the Eastern United States

Ozone production efficiency (OPE), a measure of the number of ozone (O3) molecules produced per emitted NOX (NO + NO2) molecule, helps establish the relationship between NOX emissions and O3 formation. We estimate long-term OPE variability across the eastern United States using two novel approaches: an observation-based empirical method and a chemical transport model (CTM) method. The CTM approach explicitly controls for differing O3 and NOX reaction product (NOZ) deposition rates and separately estimates OPEs from on-road mobile and electricity generating unit sources across a broad spatial scale. We find lower OPEs in urban areas and that average July OPE increased over the eastern United States domain between 2001 and 2011 from 11 to 14. CTM and empirical approaches agree at low NOZ concentrations, but CTM OPEs are greater than empirical OPEs at high NOZ. Our results support that NOX emissions reductions become more effective at reducing O3 at lower NOZ concentrations. Electricity generating unit OPEs are higher than mobile OPEs except near emissions locations, meaning further utility NOX emissions reductions will have greater per unit impacts on O3 regionally.

Estimating the Impact of Air Pollution Controls on Ambient Concentrations

This work describes the development and application of a statistical model that links electricity generating unit (EGU) and mobile source emissions with a city center monitoring cite. The model uses estimated emissions and measured concentrations over the period 2000–2012 in Atlanta, GA, USA to develop counterfactual time series of daily ozone concentrations. Further, the model estimates the sensitivity of observed ozone to each emissions sector. Results show that emissions control policies have had little effect on annual median ozone, have decreased 90th percentile ozone, and have increased 10th percentile ozone. Sensitivities to EGU and mobile emissions are compared and agree well with similar sensitivities calculated using a first-principles chemical transport model.